diff --git a/SpeedyWeatherSSM/Manifest.toml b/SpeedyWeatherSSM/Manifest.toml
new file mode 100644
index 0000000..f7455a0
--- /dev/null
+++ b/SpeedyWeatherSSM/Manifest.toml
@@ -0,0 +1,1011 @@
+# This file is machine-generated - editing it directly is not advised
+
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+    AbstractFFTsTestExt = "Test"
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+    Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
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diff --git a/SpeedyWeatherSSM/Project.toml b/SpeedyWeatherSSM/Project.toml
new file mode 100644
index 0000000..670face
--- /dev/null
+++ b/SpeedyWeatherSSM/Project.toml
@@ -0,0 +1,22 @@
+name = "SpeedyWeatherSSM"
+uuid = "0d1ea686-ee24-4a74-b4ed-044400b22b75"
+authors = ["Matt Graham"]
+version = "0.1.0"
+
+[deps]
+FillArrays = "1a297f60-69ca-5386-bcde-b61e274b549b"
+HDF5 = "f67ccb44-e63f-5c2f-98bd-6dc0ccc4ba2f"
+ParticleDA = "61cd1fb4-f4c4-4bc8-80c6-ea5639a6ca2e"
+Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
+SpeedyWeather = "9e226e20-d153-4fed-8a5b-493def4f21a9"
+
+[compat]
+ParticleDA = "1.2"
+SpeedyWeather = "0.12"
+
+[extras]
+ReadOnlyArrays = "988b38a3-91fc-5605-94a2-ee2116b3bd83"
+Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
+
+[targets]
+test = ["Test", "ReadOnlyArrays"]
diff --git a/SpeedyWeatherSSM/src/SpeedyWeatherSSM.jl b/SpeedyWeatherSSM/src/SpeedyWeatherSSM.jl
new file mode 100644
index 0000000..c0cc1ae
--- /dev/null
+++ b/SpeedyWeatherSSM/src/SpeedyWeatherSSM.jl
@@ -0,0 +1,583 @@
+module SpeedyWeatherSSM
+
+using FillArrays
+using HDF5  
+using ParticleDA
+using Random
+using SpeedyWeather
+using SpeedyWeather.RingGrids
+
+LAYERED_VARIABLES = (:vor, :div, :temp, :humid)
+SURFACE_VARIABLES = (:pres,)
+
+function equispaced_lat_lon_grid(T, n_lat, n_lon)
+    lat_interval = 180 / n_lat
+    lon_interval = 360 / n_lon
+    lat_range = (-90 + lat_interval/2):lat_interval:90
+    lon_range = (-180 + lon_interval/2):lon_interval:180
+    lat_lon_pairs = [(T(lat), T(lon)) for lat in lat_range for lon in lon_range]
+    collect(reshape(reinterpret(T, lat_lon_pairs), (2, :)))
+end
+
+Base.@kwdef struct GaussianRandomFieldParameters{T<:AbstractFloat}
+    output_scale::T = 1.
+    length_scale::T = 0.1
+end
+
+Base.@kwdef struct SpeedyParameters{T<:AbstractFloat, M<:SpeedyWeather.AbstractModel}
+    spectral_truncation::Int = 31
+    n_layers::Int = 8
+    n_days::T = 0.25
+    start_date::DateTime = DateTime(2000, 1, 1)
+    float_type::Type{T} = Float64
+    model_type::Type{M} = PrimitiveWetModel
+    observed_variable::Tuple{Symbol, Symbol} = (:physics, :precip_large_scale)
+    observed_coordinates::Matrix{T} = equispaced_lat_lon_grid(float_type, 6, 12)
+    observation_noise_std::T = 0.1
+    initial_state_grf_parameters::Dict{Symbol, GaussianRandomFieldParameters{T}} = Dict(
+        :vor => GaussianRandomFieldParameters(; output_scale=5e-7),
+        :div => GaussianRandomFieldParameters(; output_scale=5e-7),
+        :temp => GaussianRandomFieldParameters(; output_scale=2e0),
+        :humid => GaussianRandomFieldParameters(; output_scale=1e-5),
+        :pres => GaussianRandomFieldParameters(; output_scale=2e-3),
+    )
+    state_noise_grf_parameters::Dict{Symbol, GaussianRandomFieldParameters{T}} = Dict(
+        :vor => GaussianRandomFieldParameters(; output_scale=5e-8),
+        :div => GaussianRandomFieldParameters(; output_scale=5e-8),
+        :temp => GaussianRandomFieldParameters(; output_scale=2e-1),
+        :humid => GaussianRandomFieldParameters(; output_scale=1e-6),
+        :pres => GaussianRandomFieldParameters(; output_scale=2e-4),
+    )
+end
+
+struct SpeedyModel{
+    T<:AbstractFloat,
+    G<:SpeedyWeather.AbstractSpectralGrid,
+    M<:SpeedyWeather.AbstractModel,
+    I<:SpeedyWeather.RingGrids.AbstractInterpolator,
+    P<:SpeedyWeather.AbstractPrognosticVariables,
+    D<:SpeedyWeather.AbstractDiagnosticVariables
+}
+    parameters::SpeedyParameters{T, M}
+    spectral_grid::G
+    model::M
+    prognostic_variables::Vector{P}
+    diagnostic_variables::Vector{D}
+    variable_names::Tuple
+    n_layered_variables::Int
+    n_surface_variables::Int
+    n_observed_points::Int
+    observation_interpolator::I
+    initial_state_grf_scale_factors::Dict{Symbol, Vector{T}}
+    state_noise_grf_scale_factors::Dict{Symbol, Vector{T}}
+end
+
+function init(parameters::SpeedyParameters{T, M}, n_tasks::Int=1) where {
+    T<:AbstractFloat, M<:SpeedyWeather.AbstractModel
+}
+    spectral_grid = SpectralGrid(;
+        NF=T, trunc=parameters.spectral_truncation, nlayers=parameters.n_layers
+    )
+    feedback = SpeedyWeather.Feedback(; verbose=false)
+    model = M(; spectral_grid, feedback)
+    model.output.active = false
+    simulation = initialize!(model; time=parameters.start_date)
+    (; prognostic_variables, diagnostic_variables) = simulation
+    SpeedyWeather.set_period!(
+        prognostic_variables.clock, SpeedyWeather.Day(parameters.n_days)
+    )
+    SpeedyWeather.initialize!(prognostic_variables.clock, model.time_stepping)
+    # We need separate copies of prognostic and diagnostic variables for each task to
+    # allow independent parallel read-write access
+    per_task_prognostic_variables = Vector{typeof(prognostic_variables)}(undef, n_tasks)
+    per_task_diagnostic_variables = Vector{typeof(diagnostic_variables)}(undef, n_tasks)
+    per_task_prognostic_variables[1] = prognostic_variables
+    per_task_diagnostic_variables[1] = diagnostic_variables
+    for t in 2:n_tasks
+        per_task_prognostic_variables[t] = PrognosticVariables(spectral_grid, model)
+        copy!(per_task_prognostic_variables[t], prognostic_variables)
+        per_task_diagnostic_variables[t] = DiagnosticVariables(spectral_grid)
+    end
+    variable_names = SpeedyWeather.prognostic_variables(model)
+    n_layered_variables = count(
+        SpeedyWeather.has(model, var) for var in LAYERED_VARIABLES
+    )
+    n_surface_variables = count(
+        SpeedyWeather.has(model, var) for var in SURFACE_VARIABLES
+    )
+    n_observed_points = size(parameters.observed_coordinates, 2)
+    observation_interpolator = SpeedyWeather.AnvilInterpolator(
+        T, spectral_grid.Grid, spectral_grid.nlat_half, n_observed_points
+    )
+    SpeedyWeather.RingGrids.update_locator!(
+        observation_interpolator,
+        parameters.observed_coordinates[1, :],
+        parameters.observed_coordinates[2, :]
+    )
+    initial_state_grf_scale_factors = Dict(
+        name => get_grf_coefficient_scale_factors(
+            parameters.initial_state_grf_parameters[name],
+            parameters.spectral_truncation,
+            model.spectral_transform.norm_sphere
+        )
+        for name in variable_names
+    )
+    state_noise_grf_scale_factors = Dict(
+        name => get_grf_coefficient_scale_factors(
+            parameters.state_noise_grf_parameters[name],
+            parameters.spectral_truncation,
+            model.spectral_transform.norm_sphere
+        )
+        for name in variable_names
+    )
+    return SpeedyModel(
+        parameters,
+        spectral_grid,
+        model,
+        per_task_prognostic_variables,
+        per_task_diagnostic_variables,
+        variable_names,
+        n_layered_variables,
+        n_surface_variables,
+        n_observed_points,
+        observation_interpolator,
+        initial_state_grf_scale_factors,
+        state_noise_grf_scale_factors
+    )
+end
+
+function ParticleDA.get_state_dimension(model::SpeedyModel)
+    (model.parameters.spectral_truncation + 1)^2 * (
+        model.parameters.n_layers * model.n_layered_variables + model.n_surface_variables
+    )
+end
+
+function ParticleDA.get_observation_dimension(model::SpeedyModel)
+    model.n_observed_points
+end
+
+function update_spectral_coefficients_from_vector!(
+    spectral_coefficients::AbstractVector{Complex{T}}, 
+    vector::AbstractVector{T},
+    spectral_truncation::Int
+) where {T <: AbstractFloat}
+    n_row, n_col = spectral_truncation + 2, spectral_truncation + 1
+    # First column of spectral_coefficients (order = m = 0) are real-valued and we skip
+    # last row (degree = l = n_row - 1) as used only for computing meridional derivative
+    # for vector valued fields. LowerTriangularMatrix allows vector (flat) indexing
+    # skipping zero upper-triangular entries
+    spectral_coefficients[1:n_row - 1] .= vector[1:n_row - 1]
+    # Zero entry corresponding to last row as not used for scalar fields
+    spectral_coefficients[n_row] = 0
+    # vector index is i, spectral coefficient (flat) index is j
+    i = n_row - 1
+    j = n_row
+    for col_index in 2:n_col
+        # Slice corresponding to column has non-zero entries from col_index row and we
+        # ignore last row as used only for computing meridional derivative for vector
+        # valued fields
+        slice_size = n_row - col_index
+        # Reinterpret real valued state coefficients to complex spectral coefficients
+        spectral_coefficients[j + 1:j + slice_size] .= reinterpret(
+            Complex{T}, vector[i + 1:i + 2 * slice_size]
+        )
+        # Zero entry corresponding to last row as not use for scalar fields
+        spectral_coefficients[j + slice_size + 1] = 0
+        # Update vector and spectral coefficient indices, adding 1 offset
+        # to latter to skip entries corresponding to last row
+        i = i + 2 * slice_size
+        j = j + 1 + slice_size
+    end
+end
+
+function map_over_state_vector_slices(
+    map_function::Function,
+    state::AbstractVector{T},
+    variable_names::Tuple,
+    spectral_truncation::Int,
+    n_layers::Int,
+) where {T <: AbstractFloat}
+    start_index = 1
+    dim_spectral = (spectral_truncation + 1)^2
+    for name in LAYERED_VARIABLES
+        if name in variable_names
+            for layer_index in 1:n_layers
+                end_index = start_index + dim_spectral - 1
+                map_function(
+                    view(state, start_index:end_index),
+                    name,
+                    layer_index,
+                )
+                start_index = end_index + 1
+            end
+        end
+    end
+    for name in SURFACE_VARIABLES
+        if name in variable_names
+            map_function(
+                view(state, start_index:start_index + dim_spectral - 1),
+                name,
+                nothing,
+            )
+        end
+    end
+end
+
+function map_over_spectral_coefficients_and_state_vector_slices(
+    map_function::Function,
+    prognostic_variables::SpeedyWeather.PrognosticVariables{T},
+    state::AbstractVector{T},
+    variable_names::Tuple,
+    leapfrog_step::Int
+) where {T <: AbstractFloat}
+    function outer_map_function(state_slice, name, layer_index)
+        spectral_coefficients = getproperty(prognostic_variables, name)[leapfrog_step]
+        if !isnothing(layer_index)
+            spectral_coefficients = view(spectral_coefficients, :, layer_index)
+        end
+        map_function(spectral_coefficients, state_slice)
+    end
+    map_over_state_vector_slices(
+        outer_map_function,
+        state,
+        variable_names,
+        prognostic_variables.trunc,
+        prognostic_variables.nlayers,
+    )
+end
+
+function update_prognostic_variables_from_state_vector!(
+    prognostic_variables::SpeedyWeather.PrognosticVariables{T},
+    state::AbstractVector{T},
+    variable_names::Tuple;
+    leapfrog_step::Int = 1
+) where {T <: AbstractFloat}
+    spectral_truncation = prognostic_variables.trunc
+    map_over_spectral_coefficients_and_state_vector_slices(
+        (c, v) -> update_spectral_coefficients_from_vector!(c, v, spectral_truncation),
+        prognostic_variables,
+        state,
+        variable_names,
+        leapfrog_step
+    )
+end
+
+function update_prognostic_variables_from_state_vector!(
+    model::SpeedyModel{T}, state::AbstractVector{T}, task_index::Int
+) where {T <: AbstractFloat}
+    update_prognostic_variables_from_state_vector!(
+        model.prognostic_variables[task_index],
+        state,
+        model.variable_names;
+        leapfrog_step=1
+    )
+    # Zero coefficients for second leapfrog step (corresponding to initial state)
+    update_prognostic_variables_from_state_vector!(
+        model.prognostic_variables[task_index],
+        Zeros(ParticleDA.get_state_dimension(model)),
+        model.variable_names;
+        leapfrog_step=2
+    )
+end
+
+function update_vector_from_spectral_coefficients!(
+    vector::AbstractVector{T},
+    spectral_coefficients::AbstractVector{Complex{T}},
+    spectral_truncation::Int;
+    increment::Bool = false
+) where {T <: AbstractFloat}
+    n_row, n_col = spectral_truncation + 2, spectral_truncation + 1
+    update! = increment ? (lhs, rhs) -> (lhs .+= rhs) : (lhs, rhs) -> (lhs .= rhs)
+    # First column of spectral_coefficients (order = m = 0) are real-valued and we skip
+    # last row (degree = l = n_row - 1) as used only for computing meridional derivative
+    # for vector valued fields. LowerTriangularMatrix allows vector (flat) indexing
+    # skipping zero upper-triangular entries
+    @views update!(vector[1:n_row - 1], real(spectral_coefficients[1:n_row - 1]))
+    # vector index is i, spectral coefficient (flat) index is j
+    i = n_row - 1
+    j = n_row
+    for col_index in 2:n_col
+        # Slice corresponding to column has non-zero entries from col_index row and we
+        # ignore last row as used only for computing meridional derivative for vector
+        # valued fields
+        slice_size = n_row - col_index
+        # Reinterpret complex valued spectral coefficients to extract both real and
+        # imaginary components
+        @views update!(
+            vector[i + 1:i + 2 * slice_size], 
+            reinterpret(T, spectral_coefficients[j + 1:j + slice_size])
+        )
+        # Update vector and spectral coefficient indices, adding 1 offset
+        # to latter to skip entries corresponding to last row
+        i = i + 2 * slice_size
+        j = j + 1 + slice_size
+    end
+end
+
+function update_state_vector_from_prognostic_variables!(
+    state::AbstractVector{T},
+    prognostic_variables::SpeedyWeather.PrognosticVariables{T},
+    variable_names::Tuple;
+    leapfrog_step::Int = 1
+) where {T <: AbstractFloat}
+    spectral_truncation = prognostic_variables.trunc
+    map_over_spectral_coefficients_and_state_vector_slices(
+        (c, v) -> update_vector_from_spectral_coefficients!(v, c, spectral_truncation),
+        prognostic_variables,
+        state,
+        variable_names,
+        leapfrog_step
+    )
+end
+
+function update_state_vector_from_prognostic_variables!(
+    state::AbstractVector{T}, model::SpeedyModel{T}, task_index::Int
+) where {T <: AbstractFloat}
+    update_state_vector_from_prognostic_variables!(
+        state,
+        model.prognostic_variables[task_index],
+        model.variable_names
+    )
+end
+
+function update_clock_from_time_index!(clock::SpeedyWeather.Clock, time_index::Int)
+    clock.time = clock.start + clock.n_timesteps * clock.Δt * (time_index - 1)
+    clock.timestep_counter = clock.n_timesteps * (time_index - 1)
+end
+
+function update_clock_from_time_index!(
+    model::SpeedyModel, time_index::Int, task_index::Int
+)
+    update_clock_from_time_index!(
+        model.prognostic_variables[task_index].clock, time_index
+    )
+end
+
+function get_observed_variable_field(
+    diagnostic_variables::DiagnosticVariables, model::SpeedyModel
+)
+    observed_outer, observed_inner = model.parameters.observed_variable
+    getfield(getfield(diagnostic_variables, observed_outer), observed_inner)
+end
+
+function update_prognostic_and_diagnostic_variables_from_state_vector!(
+    model::SpeedyModel{T}, state::AbstractVector{T}, task_index::Int
+) where {T <: AbstractFloat}
+    update_prognostic_variables_from_state_vector!(model, state, task_index)
+    SpeedyWeather.transform!(
+        model.diagnostic_variables[task_index],
+        model.prognostic_variables[task_index],
+        1,
+        model.model,
+        initialize=true
+    )
+end
+
+function add_noise_to_state_vector!(
+    state::AbstractVector{T},
+    spectral_truncation::Int,
+    n_layers::Int,
+    variable_names::Tuple,
+    grf_scale_factors::Dict{Symbol, Vector{T}},
+    rng::AbstractRNG
+) where {T <: AbstractFloat}
+    n_row, n_col = spectral_truncation + 2, spectral_truncation + 1
+    spectral_coefficients = SpeedyWeather.LowerTriangularMatrix{Complex{T}}(
+        undef, n_row, n_col
+    )
+    function map_function(state_slice, name, layer_index)
+        generate_random_spectral_coefficients!(
+            spectral_coefficients,
+            spectral_truncation,
+            grf_scale_factors[name],
+            rng
+        )
+        update_vector_from_spectral_coefficients!(
+            state_slice, spectral_coefficients, spectral_truncation; increment=true
+        )
+    end
+    map_over_state_vector_slices(
+        map_function,
+        state,
+        variable_names,
+        spectral_truncation,
+        n_layers,
+    )
+end
+
+function get_grf_coefficient_scale_factors(
+    parameters::GaussianRandomFieldParameters{T},
+    spectral_truncation::Int,
+    norm_sphere::T
+) where {T <: AbstractFloat}
+    el_max = spectral_truncation + 1
+    (; output_scale, length_scale) = parameters
+    norm_factor = norm_sphere * output_scale / sqrt(
+        sum((2 * el + 1) * exp(-2 * length_scale^2 * el * (el + 1)) for el in 1:el_max)
+    ) 
+    scale_factors = zeros(T, el_max)
+    # el = 1 case corresponds to mean - assume zero-mean
+    for el in 2:el_max
+        scale_factors[el] = norm_factor * exp(-length_scale^2 * el * (el + 1))
+    end
+    return scale_factors
+end
+
+function generate_random_spectral_coefficients!(
+    spectral_coefficients::AbstractVector{Complex{T}},
+    spectral_truncation::Int,
+    scale_factors::AbstractVector{T},
+    rng::AbstractRNG
+) where {T <: AbstractFloat}
+    el_m = 0
+    @inbounds for m in 1:spectral_truncation + 1
+        for el in m:spectral_truncation + 2
+            el_m += 1
+            # Don't generate coefficients in last row (used only for meridional deriv.)
+            (el == spectral_truncation + 2) && continue 
+            spectral_coefficients[el_m] = scale_factors[el] * randn(rng, Complex{T})
+        end
+    end
+end
+
+ParticleDA.get_state_eltype(model::SpeedyModel{T}) where {T<:AbstractFloat} = T
+
+ParticleDA.get_observation_eltype(model::SpeedyModel{T}) where {T<:AbstractFloat} = T
+
+function ParticleDA.sample_initial_state!(
+    state::AbstractVector{T}, model::SpeedyModel{T}, rng::R, task_index::Int=1
+) where {T<:AbstractFloat, R<:AbstractRNG}
+    SpeedyWeather.initialize!(
+        model.prognostic_variables[task_index],
+        model.model.initial_conditions,
+        model.model
+    )
+    update_state_vector_from_prognostic_variables!(state, model, task_index)
+    add_noise_to_state_vector!(
+        state,
+        model.parameters.spectral_truncation,
+        model.parameters.n_layers,
+        model.variable_names,
+        model.initial_state_grf_scale_factors,
+        rng
+    )
+end
+
+function ParticleDA.update_state_deterministic!(
+    state::AbstractVector{T}, model::SpeedyModel{T}, time_index::Int, task_index::Int=1
+) where {T<:AbstractFloat}
+    update_prognostic_variables_from_state_vector!(model, state, task_index)
+    update_clock_from_time_index!(model, time_index, task_index)
+    SpeedyWeather.time_stepping!(
+        model.prognostic_variables[task_index],
+        model.diagnostic_variables[task_index],
+        model.model
+    )
+    update_state_vector_from_prognostic_variables!(state, model, task_index)
+end
+
+function ParticleDA.update_state_stochastic!(    
+    state::AbstractVector{T}, model::SpeedyModel{T}, rng::G, task_index::Int=1
+) where {T<:AbstractFloat, G<:AbstractRNG}
+    add_noise_to_state_vector!(
+        state,
+        model.parameters.spectral_truncation,
+        model.parameters.n_layers,
+        model.variable_names,
+        model.state_noise_grf_scale_factors,
+        rng
+    )
+end
+
+function ParticleDA.get_observation_mean_given_state!(
+    observation_mean::AbstractVector{T},
+    state::AbstractVector{T},
+    model::SpeedyModel{T},
+    task_index::Int=1
+) where {T<:AbstractFloat}
+    update_prognostic_and_diagnostic_variables_from_state_vector!(
+        model, state, task_index
+    )
+    observed_field_grid = get_observed_variable_field(
+        model.diagnostic_variables[task_index], model
+    )
+    SpeedyWeather.interpolate!(
+        observation_mean, observed_field_grid, model.observation_interpolator
+    )
+end
+
+function ParticleDA.sample_observation_given_state!(
+    observation::AbstractVector{T},
+    state::AbstractVector{T},
+    model::SpeedyModel{T},
+    rng::G,
+    task_index::Int=1
+) where {T<:AbstractFloat, G<:AbstractRNG}
+    ParticleDA.get_observation_mean_given_state!(observation, state, model, task_index)
+    observation .+= (
+        model.parameters.observation_noise_std 
+        * randn(rng, T, ParticleDA.get_observation_dimension(model))
+    )
+end
+
+function ParticleDA.get_log_density_observation_given_state(
+    observation::AbstractVector{T},
+    state::AbstractVector{T},
+    model::SpeedyModel{T},
+    task_index::Int=1
+) where {T<:AbstractFloat}
+    observation_mean = Vector{T}(undef, ParticleDA.get_observation_dimension(model))
+    ParticleDA.get_observation_mean_given_state!(
+        observation_mean, state, model, task_index
+    )
+    return (
+        -sum((observation - observation_mean).^2) 
+        / (2 * model.parameters.observation_noise_std^2)
+    )
+
+end
+
+const HDF5FileOrGroup = Union{HDF5.File, HDF5.Group}
+
+function write!(
+    group::HDF5FileOrGroup, key::String, value::Union{Number, String, Array}
+)
+    attributes(group)[key] = value
+end
+
+function write!(group::HDF5FileOrGroup, key::String, value::Union{Symbol, DateTime})
+    attributes(group)[key] = string(value)
+end
+
+function write!(group::HDF5FileOrGroup, key::String, value::Type)
+    attributes(group)[key] = string(nameof(value))
+end
+
+function write!(
+    group::HDF5FileOrGroup, key::String, value::NTuple{N, T}
+) where {N, T <: Union{Number, String, Symbol, Type}}
+    subgroup = create_group(group, key)
+    for (index, val) in enumerate(value)
+        write!(subgroup, string(index), val)
+    end
+end
+
+function write!(group::HDF5FileOrGroup, key::String, value::Dict)
+    subgroup = create_group(group, key)
+    for (k, v) in value
+        write!(subgroup, string(k), v)
+    end
+end
+
+function write!(group::HDF5FileOrGroup, key::String, value)
+    subgroup = create_group(group, key)
+    for name in fieldnames(typeof(value))
+        write!(subgroup, string(name), getfield(value, name))
+    end
+end
+
+function ParticleDA.write_model_metadata(file::HDF5.File, model::SpeedyModel)
+    write!(file, "parameters", model.parameters)
+end
+
+end
diff --git a/SpeedyWeatherSSM/test/runtests.jl b/SpeedyWeatherSSM/test/runtests.jl
new file mode 100644
index 0000000..2becb45
--- /dev/null
+++ b/SpeedyWeatherSSM/test/runtests.jl
@@ -0,0 +1,144 @@
+using ParticleDA
+using Random
+using Test
+using ReadOnlyArrays
+using SpeedyWeather
+using SpeedyWeatherSSM
+
+
+@testset (
+    "equispaced_lat_lon_grid with T=$T n_lat=$n_lat, n_lon=$n_lon"
+ ) for T in (Float32, Float64), n_lat in (1, 3), n_lon in (1, 2)
+     lat_lon_pairs = SpeedyWeatherSSM.equispaced_lat_lon_grid(T, n_lat, n_lon)
+     @test lat_lon_pairs isa Matrix{T}
+     @test size(lat_lon_pairs) == (2, n_lat * n_lon)
+     @test all(-90 .<= lat_lon_pairs[1, :] .<= 90)
+     @test all(-180 .<= lat_lon_pairs[2, :] .<= 180)
+end
+
+function check_consistency_vector_and_spectral_coefficients(
+    vector::Vector{T}, spectral_coefficients::LowerTriangularMatrix{Complex{T}}
+) where {T <: AbstractFloat}
+    n_row, n_col = size(spectral_coefficients, as=Matrix)
+    # Test first (n_row - 1) entries corresponding to real component of first column
+    @test all(vector[1:n_row - 1] == real(spectral_coefficients[1:n_row - 1]))
+    # Test remaining entries correspond to interleaved real and imaginary components
+    # scanning across columns
+    vector_index = n_row
+    for col_index in 2:n_col
+        for row_index in col_index:(n_row - 1)
+            @test vector[vector_index] == real(
+                spectral_coefficients[row_index, col_index]
+            )
+            @test vector[vector_index + 1] == imag(
+                spectral_coefficients[row_index, col_index]
+            )
+            vector_index += 2
+        end
+    end
+end
+
+@testset (
+    "update_spectral_coefficients_from_vector with T = $T, trunc = $spectral_truncation"
+) for T in (Float32, Float64), spectral_truncation in (1, 7, 15)
+    rng = Random.Xoshiro(1234)
+    vector = randn(T, (spectral_truncation + 1)^2)
+    n_row, n_col = spectral_truncation + 2, spectral_truncation + 1
+    spectral_coefficients = SpeedyWeather.LowerTriangularMatrix{Complex{T}}(
+        undef, n_row, n_col
+    )
+    # Set coefficients in last row to NaN - these should be replaced with zero
+    for col_index in 1:n_col
+        spectral_coefficients[n_row, col_index] = NaN
+    end
+    # Wrap vector as read-only to check not being mutated 
+    SpeedyWeatherSSM.update_spectral_coefficients_from_vector!(
+        spectral_coefficients, ReadOnlyVector(vector), spectral_truncation
+    )
+    # Check last row entries all zero
+    for col_index in 1:n_col
+        @test spectral_coefficients[n_row, col_index] == 0
+    end
+    check_consistency_vector_and_spectral_coefficients(vector, spectral_coefficients)     
+end
+
+@testset (
+    "update_vector_from_spectral_coefficients with T = $T, trunc = $spectral_truncation"
+) for T in (Float32, Float64), spectral_truncation in (1, 7, 15)
+    rng = Random.Xoshiro(1234)
+    vector = Vector{T}(undef, (spectral_truncation + 1)^2)
+    n_row, n_col = spectral_truncation + 2, spectral_truncation + 1
+    spectral_coefficients = SpeedyWeather.LowerTriangularMatrix{Complex{T}}(
+        undef, n_row, n_col
+    )
+    # First column (order = m = 0) coefficients real-valued
+    spectral_coefficients[1:n_row] = randn(rng, T, n_row)
+    # Remainder of coefficients complex valued
+    spectral_coefficients[n_row + 1:end] = randn(
+        rng, Complex{T}, length(spectral_coefficients) - n_row
+    )
+    # Set coefficients in last row to NaN - these should be ignored
+    for col_index in 1:n_col
+        spectral_coefficients[n_row, col_index] = NaN
+    end
+    # Wrap spectral_coefficient as read-only to check not being mutated 
+    SpeedyWeatherSSM.update_vector_from_spectral_coefficients!(
+        vector, ReadOnlyVector(spectral_coefficients), spectral_truncation
+    )
+    # Check no NaNs from last row ended up in vector
+    @test !any(isnan.(vector))
+    check_consistency_vector_and_spectral_coefficients(vector, spectral_coefficients)          
+end
+
+@testset (
+    "Updating prognostic variables to/from state vector with \
+    T=$T, spectral_truncation=$spectral_truncation, n_layers=$n_layers"
+ ) for T in (Float32, Float64), spectral_truncation in (7, 15), n_layers in (1, 8)
+    rng = Random.Xoshiro(1234)
+    spectral_grid = SpectralGrid(;
+        NF=T, trunc=spectral_truncation, nlayers=n_layers
+    )
+    layered_variables = (:vor, :div, :temp, :humid)
+    surface_variables = (:pres,)
+    all_variables = (layered_variables..., surface_variables...)
+    state_dimension = (spectral_truncation + 1)^2 * (
+        n_layers * length(layered_variables) + length(surface_variables)
+    )
+    state = rand(rng, T, state_dimension)
+    prognostic_variables_1 = SpeedyWeather.PrognosticVariables(spectral_grid)
+    prognostic_variables_1_init = SpeedyWeather.PrognosticVariables(spectral_grid)
+    SpeedyWeather.copy!(prognostic_variables_1_init, prognostic_variables_1)
+    prognostic_variables_2 = SpeedyWeather.PrognosticVariables(spectral_grid)
+    SpeedyWeatherSSM.update_prognostic_variables_from_state_vector!(
+        prognostic_variables_1, state, all_variables
+    )
+    SpeedyWeatherSSM.update_prognostic_variables_from_state_vector!(
+        prognostic_variables_2, state, all_variables
+    )
+    # Check all variables changed from initial values
+    for variable_name in all_variables
+        @test all(
+            getproperty(prognostic_variables_1, variable_name)[1]
+            != getproperty(prognostic_variables_1_init, variable_name)[1]
+        )
+    end
+    # Check variables updates from same state vector match
+    for variable_name in all_variables
+        @test all(
+            getproperty(prognostic_variables_1, variable_name)[1]
+            == getproperty(prognostic_variables_2, variable_name)[1]
+        )
+    end
+    # Check state vector reconstructed from prognostic variables matches original
+    state_2 = Vector{T}(undef, state_dimension)
+    SpeedyWeatherSSM.update_state_vector_from_prognostic_variables!(
+        state_2, prognostic_variables_1, all_variables
+    )
+    @test all(state == state_2)
+end
+    
+@testset "Generic model interface unit tests" begin
+    seed = 1234
+    model = SpeedyWeatherSSM.init(SpeedyWeatherSSM.SpeedyParameters())
+    ParticleDA.run_unit_tests_for_generic_model_interface(model, seed)
+end